Analysis and Optimization of Performance Parameters in ... · TG ethanolysis within a global analysis –feed stocks–conversion–engine. Hence, non-catalytic supercritical ethanolysis

Mechanics and Mechanical EngineeringVol. 21, No. 2 (2017) 193–205c⃝ Lodz University of Technology

Analysis and Optimization of Performance Parametersin Computerized I.C. Engine Using Diesel Blended with Linseed Oil

and Leishmaan’s Solution

A. Godwin AntonyS. Dinesh

K. RajaguruV. Vijayan

Department of Mechanical EngineeringK. Ramakrishnan College of Technology

Tiruchirapalli, Tamil Nadu, [email protected]

S. Aravind

Department of Mechanical EngineeringK. Ramakrishnan College of Engineering

Tiruchirapalli, Tamil Nadu, India

Received (21 December 2016)Revised (6 January 2017)

Accepted (11 February 2017)

Due to environmental concerns, the use of alternative fuel is rapidly expanding aroundthe world, making it imperative to fully understand the impacts of diesel on pollutantformation. The aim of this work is to increase the engine performance with reducedemissions using diesel blends from different feed stocks and to compare that with thediesel. In the Indian context linseed can play an important role in the production ofalternative fuel using diesel. The climatic and soil conditions of India are convenientfor the production of linseed crop. The blend is prepared from diesel, linseed oil andLeishman‘s solution by stirring process. The performance study of a diesel engine withthese diesel blends were carried out at different compression ratios and loads. The com-bustion performance parameters like Mechanical, Volumetric, Brake thermal efficienciesand Specific Fuel Consumption are all noted. By applying Grey Relation Analysis, thebest running condition for the engine within the chosen range are decided. The samplecontaining 95% diesel with 3%linseed oil and 2% solution gives better performance ata compression ratio of 18 during high load conditions.

Keywords: Diesel blend, linseed oil, performance test, Grey Relation Analysis.

1. Introduction

M. P. Sharma et al [1] experimented with the Biodiesel, derived from the trans–esterification of vegetable oils or animal fats. It is composed of saturated and unsat-

194 Godwin, A. A., Aravind, S., Dinesh, S., Rajaguru, K. and Vijayan, V.

urated long-chain fatty acid alkyl esters. In spite of having some application prob-lems, it is being considered as one of the most promising alternative fuels in internalcombustion engine. The work presented a comprehensive review of engine perfor-mance and emissions using those biodiesel. The use of biodiesel will lead to lossin engine power mainly due to the reduction in heating value and it increased fuelconsumption. R. Vallinayagam et al [2] emanated the conceptualization to improvethe performance and emission characteristics of diesel engine fueled with biodiesel,by altering the fuel properties through the addition of additive 1,4–Dioxane (a mul-tipurpose additive). It has been zeroed in as one of the indispensable additive tobe added with the optimum blend of KME (kapokmethyl ester), a biodiesel pro-duced from under-utilized kapok oil. Interestingly, all the reported emissions forB25 with additive were much lower than diesel fuel, making it as one of the ap-propriate fuel blend for diesel engine. Michelle Sergent et al [3] improved engineperformance through better fuel efficiency and reduced exhaust emissions, throughthe blending of petro diesel, biodiesel, bioethanol and water in various proportions.This focuses specifically on the production of biodiesel fuels by supercritical (Sc)TG ethanolysis within a global analysis –feed stocks–conversion–engine. Hence,non-catalytic supercritical ethanolysis of TG, combined with —glycerol valorizationand —materials (feed stock residues/ glycerol/ biodiesel) heat power cogenerationadmitting all these feed stocks is thus a viable conversion process for reaching theobjective of a sustainable biodiesel alternative.

S. Jayaraj et al [4] identified that the lower blends of biodiesel increase the brakethermal efficiency and reduce the fuel consumption. The exhaust gas emissions arereduced with increase in biodiesel concentration. The production and characteriza-tion of vegetable oil as well as the experimental work carried out in various countriesare summarized. The two-step esterification process converts the crude high FFArubber seed oil to a more suitable form of fuel for diesel engines. Md. Abul Kalamet al [5] proved that a complete substitution of petroleum derived fuels by biofuelis impossible from the production capacity and engine compatibility point of view.Some guidelines on dedicated biofuel engine are prescribed. The hunt for an alter-native clean fuel is vital. Till date energy from wind, solar, tidal and fusion are allvery prospective type of renewable energy. Tripathi Neha et al [6] searched viablealternatives to the fossil fuel. FAME (Fatty Acid Methyl Ester) an environmentfriendly alternative, commonly produced by the transesterification process may betermed as biodiesel because of the characters like high flash point, biodegradable,nontoxic, etc. This attempt compares two production process viz. base catalyzedand two stage acid–base catalyzed for two possible feed stocks viz. sunflower oiland cottonseed oil, using methanol with catalysts sodium hydroxide and potassiumhydroxide.

Alexandra Gruia et al [7] evaluated the characteristics of linseed oil to determinewhether that could be exploited as an edible oil. Petroleum ether extraction oflinseeds produced yields of 30% (w/w) oil. The chemical composition was foundto contain high levels of linolenic (53.21%), followed by oleic (18.51%), and linoleic(17.25%), while the dominant saturated acids were palmitic (6.58%) and stearic(4.43%).Regarding fatty acid composition, the saturate composed an average of11.01% of the total fatty acids and of 88.97% unsaturated acid. The major saturatedfatty acid was palmitic (16:0) and stearic acid (C18:1).The major unsaturated fatty

Analysis and Optimization of Performance Parameters ... 195

acid in the oil samples was linolenic (18:3), followed by oleic (18:1) and linoleic(18:2). Chauhan. Y. Rita et al [8] investigated about the production of renewableenergy globally since it is increasingly understood that first generation biofuels,mostly oil seeds are limited in their ability to achieve targets for biofuel production,climate change mitigation and economic growth. This work assesses and integratesabout the different tree borne oilseeds, extraction of oil, biodiesel processing andeffect of different parameters on production of biodiesel.A.K.Pradhan et al [9] studied the production process, fuel properties, oil content,engines testing and performance analysis of biodiesel from karanja oil which is knownas Karanja oil methyl ester (KOME). The oil extraction was found 35% by n–hexanemethod. Lower calorific value indicates more oil consumption. The specific gravity,kinematic viscosity of B20 and B40 blends is much closer to diesel. At higher loadmaximum brake power was observed for B20 blend. BSFC reduces with increasein load, BTE shows better result than diesel. Incomplete combustion of KOME ismuch lower than diesel which was indicated in smoke opacity curve. M. Pandianet al [10] aimed at investigating the effect of injection system parameters such asinjection pressure, injection timing and nozzle tip protrusion on the performanceand emission characteristics of a twin cylinder water cooled naturally aspirated CIDIengine. Biodiesel, derived from pongamia seeds through transesterification process,blended with diesel was used as fuel. The experiments were designed based onresponse surface methodology (RSM).The resultant models of the response surfacemethodology were helpful to predict the response parameters. S. Naga Prasadobserved that 25% of neat oil mixed with 75% of diesel is the best suited blend,without heating and without any modification of the engine. Methyl ester of Linseedoil is the better performing fuel due to better performance and lower emissionscompared to other chosen methyl esters. Brake Specific Fuel Consumption forlinseed oils is increased compared to diesel at rated load and is result of delay inignition process.

2. Experimental set up

2.1. Blending of fuel

The blends of varying proportion of linseed oil, lieshman solution and diesel wereprepared, analyzed and compared. From properties and engine test result it can beestablished that oil can be substituted for diesel without any engine modificationand without preheating of the blends. The various blend proportions chosen forthe present work are listed in the Tab. 1. The blending process followed is called”stirring method”.

Table 1 Combinations used in the experiment

Sample ProportionsS1 1000ml of DS2 950ml of diesel + 30ml of LO +20ml of LSSS3 900ml of diesel + 50ml of LO + 50ml of LSSD- Diesel; L- Linseed oil; LSS - Leishman’s solution


Table 2 Property values of the various blends of the diesel

S. No Properties S1 S2 S31 Flash Point (◦C) 57 35 472 Fire Point (◦C) 65 43 553 Density (kg/m3) 800 817 829

2.2. Engine setup

The setup consists of single cylinder, four stroke, Multi–fuel, research engine con-nected to eddy current type dynamometer for loading. The operation mode of theengine can be changed from diesel to Petrol or from Petrol to Diesel with some nec-essary changes. In both modes the compression ratio can be varied without stoppingthe engine and without altering the combustion chamber geometry by specially de-signed tilting cylinder block arrangement. The various specifications of the engineis given below.

Figure 1 Layout of the engine set up

2.2.1. Engine specification:

1. EngineType: 1 cylinder, 4 stroke, stroke 110 mm, bore 87.5 mm

2. Capacity: 661 cc

3. Dynamometer: Type eddy current, water cooled, with loading unit

4. Propeller shaft: With universal joints

5. Fuel tank Capacity: 15 lit, Type: Duel compartment, with fuel metering pipe

6. Calorimeter Type: Pipe in pipe


7. Load indicator: Digital, Range 0-50 kg, Supply 230 VAC

8. Load sensor: Load cell, type strain gauge, range 0-50 kg

9. Software: “Enginesoft” Engine performance analysis software

10. Rota meter: Engine cooling 40-400 LPH; Calorimeter 25-250 LPH

11. Pump Type: Monoblock

12. Overall dimensions: W 2000 x D 2500 x H 1500 mm

2.3. Input parameters combination

The input parameters chosen for the present study are the fuel sample, compressionratio and load. There are other parameters also available for combustion processstudy. But the chosen parameters seem to have significant effect on combustion.There are three levels in each of the input parameter. This leads to a combinationof 27 experimental runs following the full factorial design approach. The variouscombinations of the input parameters are listed in Tab. 3.

3. Results

The output parameters of Specific fuel consumption, Brake thermal efficiency, Me-chanical efficiency and Volumetric efficiency are noted. These outputs are more de-sirable than the rest responses because of higher degree of performance is achievedthrough improvising them. The experiment were conducted in the order as men-tioned in Tab. 3. The output values are listed in the table 4. It is found that theperformance of diesel engine is good at compression ratios 17:1 and 18:1. Whereasit is not up to the expected level at the compression ratio 17.5:1.

3.1. Specific fuel consumption

The specific fuel consumption of the diesel engine must be lower in order to attainhigh performance. The Fig. 2 is drawn according to values obtained from the testsconducted. From the plot, it is clear that the sample 2 (containing 95% diesel with3% linseed oil and 2% leishman’s solution) has lower SFC at a compression ratio18. Other fuel compositions at different compression ratio are also presented but allhave higher fuel consumption values than the former. It is easily noted that dieselhas more fuel consumption in its sole form than the blended form.


Table 3 Chosen input parameter combinations

Experiment no. Sample Compression Ratio Load1 S1 17.5 32 S1 18 93 S1 17.5 64 S1 17 95 S1 17 36 S1 17 67 S1 17.5 98 S1 18 39 S1 18 610 S2 18 311 S2 18 912 S2 17 913 S2 18 614 S2 17.5 315 S2 17 616 S2 17 317 S2 17.5 618 S2 17.5 919 S3 17 620 S3 17 921 S3 17.5 922 S3 18 923 S3 17.5 624 S3 17.5 325 S3 18 326 S3 18 627 S3 17 3

3.2. Brake thermal efficiency

The Fig. 3 depicts about the brake thermal efficiency obtained at various compres-sion ratios using different fuel blends. The value of ηbt is lower for diesel in its soleform at all the compression ratios. The blended compounds have produced a higherηbt even at lower compression state. The sample 2 (containing 95% diesel with 3%linseed oil and 2% Leishman’s solution) has produced higher ηbt at a compressionratio of 18. The sample 3 (containing 90% diesel with 5% linseed oil and 5% leish-man’s solution) have produced a good result consistently at all the compressionstages, however the maximum is achieved by the sample 2.


Table 4 Output values corresponding to the input parameter combination

Experiment no. SFC ηbt ηmech ηvol[kg/kWh] [%] [%] [%]

1 0.79 10.26 21.58 80.862 0.33 24.37 49.23 78.983 0.48 16.79 35.8 80.394 0.39 20.9 45.69 79.115 0.78 10.37 19.15 80.626 0.49 16.4 34.37 80.087 0.36 22.69 47.2 79.98 0.66 12.23 23.26 80.199 0.42 19.25 35.88 79.5410 0.52 16.2 26.37 77.7211 0.29 29.13 56.61 77.3512 0.3 27.8 56.93 77.3913 0.38 22.55 46.32 77.6514 0.6 14.24 30.03 78.2515 0.38 22.57 44.76 78.0116 0.59 14.33 27.25 78.0917 0.39 21.91 45 78.1818 0.31 27.41 55.56 77.419 0.38 22.57 47.76 78.0120 0.3 27.8 56.93 77.3921 0.31 27.41 55.56 77.422 0.31 27.41 55.56 77.423 0.39 21.91 45 78.1824 0.6 14.24 30.03 78.2525 0.6 14.24 30.03 78.2526 0.39 21.91 45 78.1827 0.59 14.33 27.25 78.09

3.3. Mechanical efficiency

The mechanical efficiency of the diesel engine is found to be higher for the blendedfuels than diesel at all the compression stages. The Fig. 4 represents mechanicalefficiency plotted against the load. From the plot, it is understandable that theηmech is very high for the sample 3 at higher compression ratios. The sample 2has also produced a comparable result as that of sample 3 at higher compressionratio. Hence, it is understood that the compression ratio increase will provide highermechanical efficiency.


Figure 2 Response values of Specific Fuel Consumption at various loads for each sample

Figure 3 Response values of Brake Thermal Efficiency at various loads for each sample

3.4. Volumetric efficiency

The volumetric efficiency of the engine is found to be higher at the compressionratio 17.5 for diesel fuel. The figure 6 represents the volumetric efficiency plottedagainst load. The ηvol has to be high for any engine to produce a good combustionperformance. The diesel blends however a lower ηvol than diesel has. The dieselfuel initially has an increasing effect on ηvol for improved compression ratio buttends to reduce at higher stages. Both the blends (sample 2 & 3) seem to producean equal value of ηvol at each load and compression ratio combinations. There isonly a slight deviation between the results and so the curves overlap one another inFig. 5.


Figure 4 Response values of Mechanical Efficiency at various loads for each sample

Figure 5 Response values of volumetric efficiency at various loads for each sample

4. Optimization using GRA

The Grey relational analysis is a method of measuring the degree of approximationamong sequences according the Grey Relational Grade. The measured output val-ues of the experiment like Mechanical, Volumetric, Brake thermal efficiencies andSpecific Fuel Consumption are normalized in the range between 0 and 1.The opti-mization of the complicated multiple performance characteristics can be convertedinto optimization of single grey relational grade.When the range of the sequence is large or the standard value is enormous, thefunction of factors is neglected. If the factors goal and direction are different,then the data must be pre–processed to a group of sequence called ”grey relationalgeneration”. Data pre–processing is a process of transferring the original sequenceto a comparable sequence.


Table 5 Grey Relation coefficient values for corresponding output

Exp.No.

GRCSFC

GRCηbt

GRCηmech

GRCηvol

TotalGRC

GreyRelationGrade

Rank

1 0.3333 0.3333 0.3483 1.0000 2.0149 0.50373 192 0.8621 0.6647 0.7104 0.4828 2.7200 0.67999 73 0.5682 0.4333 0.4720 0.7888 2.2623 0.56556 164 0.7143 0.5341 0.6269 0.5007 2.3761 0.59401 105 0.3378 0.3346 0.3333 0.8797 1.8855 0.47138 206 0.5556 0.4257 0.4557 0.6923 2.1293 0.53232 187 0.7813 0.5943 0.6600 0.6464 2.6820 0.67050 88 0.4032 0.3583 0.3594 0.7237 1.8446 0.46115 219 0.6579 0.4885 0.4730 0.5707 2.1901 0.54752 1710 0.5208 0.4219 0.3820 0.3585 1.6832 0.42081 2211 1.0000 1.0000 0.9833 0.3333 3.3167 0.82917 112 0.9615 0.8765 1.0000 0.3359 3.1739 0.79347 213 0.7353 0.5891 0.6403 0.3535 2.3182 0.57956 1114 0.4464 0.3879 0.4125 0.4021 1.6489 0.41222 2315 0.7353 0.5899 0.6082 0.3811 2.3145 0.57861 1216 0.4545 0.3893 0.3889 0.3878 1.6206 0.40516 2617 0.7143 0.5665 0.6129 0.3957 2.2894 0.57235 1318 0.9259 0.8458 0.9324 0.3365 3.0406 0.76016 419 0.7353 0.5899 0.6732 0.3811 2.3795 0.59487 920 0.9615 0.8765 1.0000 0.3359 3.1739 0.79347 321 0.9259 0.8458 0.9324 0.3365 3.0406 0.76016 522 0.9259 0.8458 0.9324 0.3365 3.0406 0.76016 623 0.7143 0.5665 0.6129 0.3957 2.2894 0.57235 1424 0.4464 0.3879 0.4125 0.4021 1.6489 0.41222 2425 0.4464 0.3879 0.4125 0.4021 1.6489 0.41222 2526 0.7143 0.5665 0.6129 0.3957 2.2894 0.57235 1527 0.4545 0.3893 0.3889 0.3878 1.6206 0.40516 27

For this purpose the experimental results are normalized in the range betweenzero and one. The normalization can be done in various forms.

1. If the target value has a characteristic of ”the larger–the–better”:

xi(k) =xi(k)−min(x0

i (k))

max(x0i (k))−min(x0

i (k))

2. If the target value has a characteristic of ”smaller–the better”:

xi(k) =max(xi(k))− x0

i (k)

max(x0i (k))−min(x0

i (k))


Following data pre–processing, a grey relational coefficient is calculated to expressthe relationship between the ideal and actual normalized experimental results. Thegrey relation coefficients of the present work is listed in the Tab. 5. After obtainingthe grey relational coefficient, we normally take the average of the grey relationalcoefficient as the grey relational grade. The rank for various GRG is awarded onthe basis of maximum to minimum value, as it depicts the order of desirability. Thefinalized average GRG value for the individual level of the input parameters is givenin Tab. 6. The Fig. 6 depicts the selected level of each input for attaining bestcombustion performance based on the average Grey Relation Grade value.

Table 6 Optimized input parameter levels based on the GRG

Sample CompressionRatio

Load

Level1

5.026 5.168 3.904

Level2

5.352 5.229 5.115

Level3

5.283 5.263 6.641

Figure 6 Optimized level values chosen based on the GRG

5. Conclusion

In Fig. 2–5, better combustion performance is explained clearly depicting about thecontribution of various samples, loads and compression ratios individually. By theGrey Relation Analysis optimization procedure, the desirability of the combinationof inputs for attaining the best possible output is also listed. From the average GreyRelation Grade value of each level it is concluded that sample 2 (containing 95%


diesel with 3% linseed oil and 2% Leishman’s solution by volume) at a compressionratio of 18:1 will produce excellent performance in high load conditions. In future,several alternates of diesel additive can be investigated to attain higher combustioncharacteristics with reduced emission levels.

References

[1] Sigar, C P., Soni, S. L., Mathur, J. and Sharma, D.: Performance and emissioncharacteristics of vegetable oil as diesel fuel extender, Energy Source Part, 31, 139–48,2009.

[2] Vallinayagam, R.: Effect of adding 1, 4-Dioxane with kapok biodiesel on the char-acteristics of a diesel engine, Applied Energy, 136, 1166–1173, 2014.

[3] Sergent, M.: Biodiesel via supercritical ethanolysis within a global analysis ”feed-stock – conversion – engine” for a sustainable fuel alternative, Article in Progress inEnergy and combustion science, 2014.

[4] Jayaraj, S., Ramadhas, A. S. and Muraleedharan, C.: Biodiesel productionfrom high FFA rubber seed oil, Elsevier, 84, 4, 335–340, 2005.

[5] Kalam, A.: An overview of biofuel as a renewable energy source: development andchallenges, Procedia Engineering, 56, 39–53, 2013.

[6] Neha, T.: Use of Sunflower and Cottonseed Oil to prepare Biodiesel by catalystassisted Transesterification, 3, 3, 42–47, 2013.

[7] Gruia, A.: Fatty acids composition and oil characteristics of linseed (Linum Usitatis-simum L.) from Romania, Journal of Agroalimentary Processes and Technologies, 18,2, 136–140, 2012.

[8] Chauhan, Y. R.: Biodiesel production from non-edible-oils” Journal of Chemicaland Pharmaceutical Research, 4, 9, 4219–4230, 2012.

[9] Pradhan, A.K.: Non–Edible Karanja Biodiesel A Sustainable Fuel for C.I. Engine,2, 6, 853–860, 2012.

[10] Pandian, M.: Investigation on the effect of injection system parameters on per-formance and emission characteristics of a twin cylinder compression ignition directinjection engine fuelled with pongamia biodiesel–diesel blend using response surfacemethodology, Applied Energy, 88, 2663–2676, 2011.

[11] Arcoumanis, C., Bae, C., Crookes, R. and Kinoshita, E.: The potential of di-methyl ether (DME) as an alternative fuel for compression-ignition engines: a review,Fuel, 87, 1014–30, 2008.

[12] Agrawal, A.K. and Das, L. M.: Biodiesel development and characterization foruse as a fuel in compression ignition engines, Trans ASME, 1, 123, 440–7.

[13] Agarwal, A.K.: Vegetable oils versus diesel fuel: development and use of biodieselin a compression ignition engine, TERI Inf. Digest on Energy, 8, 191–204, 1998.

[14] Agarwal, A. K. and Das, L. M.: Biodiesel development and characterization foruse as a fuel in compression ignition engines, Am Soc. Mech. Engrs., J Eng., Gas,Turb. Power, 123, 440–7, 2000.

[15] Das, L.M.: Combustion analysis of Jatropha, Karanja and Polanga based biodieselas fuel in a diesel engine, Fuel, 88, 994–999, 2009.

[16] Fangrui, M. and Milford, A.: Bio–diesel production—review, Bioresour. Technol.,1999.

[17] Ghobadian, B., Rahimi, H., Nikbakht, A. M., Najafi, G. and Yusaf, T.F.: Diesel engine performance and exhaust emission analysis using waste cookingbiodiesel fuel with an artificial neural network, Renewable Energy, 34, 976–982, 2009.


[18] Kumar, N. R.: Comparative evaluation of performance and emission characteristicsof jatropha, karanja and polanga based biodiesel as fuel in a tractor engine, Fuel, 88,1698–1707, 2009.

[19] Balat, M.: Potential alternatives to edible oils for biodiesel production – A reviewof current work, Energy Conversion and Management, 52, 1479–1492, 2011.

[20] Pradhan, A.K.: Non–Edible Karanja Biodiesel- A Sustainable Fuel for C.I., Engine,2, 6, 853–860, 2012.

[21] Dincer, K.: Lower emissions from biodiesel combustion, Energy Source, Part A 30,963–8, 2008.

Documents

Analysis and Optimization of Performance Parameters in ... · TG ethanolysis within a global analysis –feed stocks–conversion–engine. Hence, non-catalytic supercritical ethanolysis